As is well known, considerable effort is spent in academia and industry towards developing techniques enabling the detection of defects in wafers before their cleavage into distinct dies, capsulation and subsequent distribution to the marketplace in the form of chips. Preliminary defect detection inter alia improves the "yield" in terms of discarding defective wafers and consequently increasing the percentage of fault-free chips delivered from the production line.
In recent years, defect detection has been improved by techniques for classifying defects into distinct defect types. These techniques not only allow identification of defective wafers so as to increase the yield, but also to provide some information on the cause of the defects. The production stage can then be reconfigured or modified in order to produce a better wafer series.
The improved sensitivity accomplished by classifying defects is illustrated graphically in FIG. 1 (10), which shows the number of defects (Z) of various types (Y) for four sequentially produced wafers (X). Whilst the total count (12) is essentially the same for the four wafers, the count for defect type D (14) dramatically increased over time. This allows the cause of the defect to be identified and corrected.
A rudimentary defect classification method involves a person visually inspecting wafers for defects and classifying the defects according to predetermined criteria. This manual procedure is not only slow but also error prone, since it relies on the inspector's professional skills which obviously differ from one inspector to another.
Automatic Defect Classification (ADC) techniques cope in many respects with the shortcomings of the manual procedures. Thus, for example.
KLA of Santa Clara, Calif., markets a software package, called Impact, as an add-on option to its inspection-review systems, such as the Indy 2230. The software applies certain algorithm to the defect image taken by a CCD camera to attempt and classify the defect. Notably, the Impact algorithm can be operated only when the system is in the review mode, and is inoperable when the system is in the inspection mode. Thus, in order to classify the defects, the system first has to scan the entire wafer in the inspection mode, switch to the review mode and re-visit the suspected sites identified during the inspection mode, take a magnified image of the defect and a reference site, and only then apply the ADC algorithm to classify the defect. The latter mode of operation is generally known in the industry as "re-visit ADC". Also notably, the system uses only a single image of the defect from a single perspective.
Whilst the CCD image based analysis is more accurate and reproducible than the manual procedure (i.e. virtually identical results ate obtained by repeated analysis of the same wafer), it still has some major shortcomings, e.g. it is time consuming. It is important to note in this context that a prolonged wafer inspection session adversely affects the entire production line throughout. Unduly slowing down the production line throughput for wafer inspection is, in many cases, commercially infeasible from a cost perspective, considering the high cost of the chip fabrication process.
There is accordingly, a need in the art to provide for an ADC technique that will provide a relatively accurate and reproducible defect classification, and that at the same time, will substantially reduce the duration of the defect classification stage of hitherto known devices. To this end, in the present invention, defect classification is substantially incorporated into the wafer inspection phase so as to constitute an on-the-fly ADC, i.e. the ADC is inspected as the wafer is scanned for defects.
Throughout this specification, a use is made of the convention that "inspection" refers to the process wherein a substrate is scanned to identify locations suspected of having defects thereon, whilst "review" refers to the process wherein the suspected locations are revisited to confirm/refute the presence of a defect in the suspect location and investigate the defect should such indeed exists, all as known per se.